Conference proceeding
Classification and Detection of Micro-Level Impact of Issue-Focused Documentary Films based on Reviews
CSCW'17: PROCEEDINGS OF THE 2017 ACM CONFERENCE ON COMPUTER SUPPORTED COOPERATIVE WORK AND SOCIAL COMPUTING, pp 1419-1431
01 Jan 2017
Abstract
We present novel research at the intersection of review mining and impact assessment of issue-focused information products, namely documentary films. We develop and evaluate a theoretically grounded classification schema, related codebook, corpus annotation, and prediction model for detecting multiple types of impact that documentaries can have on individuals, such as change versus reaffirmation of behavior, cognition, and emotions, based on user-generated content, i.e., reviews. This work broadens the scope of review mining tasks, which typically comprise the prediction of ratings, helpfulness, and opinions. Our results suggest that documentaries can change or reinforce peoples' conception of an issue. We perform supervised learning to predict impact on the sentence level by using data driven as well as predefined linguistic, lexical, and psychological features; achieving an accuracy rate of 81% (F1) when using a Random Forest classifier, and 73% with a Support Vector Machine.
Metrics
Details
- Title
- Classification and Detection of Micro-Level Impact of Issue-Focused Documentary Films based on Reviews
- Creators
- Rezvaneh Rezapour - University of Illinois Urbana-ChampaignJana Diesner - University of Illinois Urbana-ChampaignAssoc Comp Machinery
- Publication Details
- CSCW'17: PROCEEDINGS OF THE 2017 ACM CONFERENCE ON COMPUTER SUPPORTED COOPERATIVE WORK AND SOCIAL COMPUTING, pp 1419-1431
- Publisher
- Assoc Computing Machinery
- Number of pages
- 13
- Grant note
- 0155- 0370 / FORD Foundation National Center of Supercomputing Applications (NCSA) at UIUC
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Information Science (Informatics)
- Web of Science ID
- WOS:000455087800105
- Scopus ID
- 2-s2.0-85014780759
- Other Identifier
- 991021861818804721
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Web of Science research areas
- Computer Science, Interdisciplinary Applications
- Social Sciences, Interdisciplinary